標題: Towards high performance data analytic on heterogeneous many-core systems: A study on Bayesian Sequential Partitioning
作者: Lai, Bo-Cheng
Wu, Tung-Yu
Chiu, Tsou-Han
Li, Kun-Chun
Lee, Chia-Ying
Chien, Wei-Chen
Wong, Wing Hung
交大名義發表
National Chiao Tung University
關鍵字: Data processing;Heterogeneous system;Many-core system;Performance analysis;Design and optimization
公開日期: 1-Dec-2018
摘要: Bayesian Sequential Partitioning (BSP) is a statistically effective density estimation method to comprehend the characteristics of a high dimensional data space. The intensive computation of the statistical model and the counting of enormous data have caused serious design challenges for BSP to handle the growing volume of the data. This paper proposes a high performance design of BSP by leveraging a heterogeneous CPU/GPGPU system that consists of a host CPU and a K80 GPGPU. A series of techniques, on both data structures and execution management policies, is implemented to extensively exploit the computation capability of the heterogeneous many-core system and alleviate system bottlenecks. When compared with a parallel design on a high-end CPU, the proposed techniques achieve 48x average runtime enhancement while the maximum speedup can reach 78.76x. (C) 2018 Elsevier Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/j.jpdc.2018.07.011
http://hdl.handle.net/11536/148358
ISSN: 0743-7315
DOI: 10.1016/j.jpdc.2018.07.011
期刊: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
Volume: 122
起始頁: 36
結束頁: 50
Appears in Collections:Articles